A metabolic network-based approach for developing feeding strategies for CHO cells to increase monoclonal antibody production

Chinese hamster ovary (CHO) cells are the main workhorse in the biopharmaceutical industry for the production of recombinant proteins, such as monoclonal antibodies. To date, a variety of metabolic engineering approaches have been used to improve the productivity of CHO cells. While genetic manipulations are potentially laborious in mammalian cells, rational design of CHO cell culture medium or efficient fed-batch strategies are more popular approaches for bioprocess optimization. In this study, a genome-scale metabolic network model of CHO cells was used to design feeding strategies for CHO cells to improve monoclonal antibody (mAb) production. A number of metabolites, including threonine and arachidonate, were suggested by the model to be added into cell culture medium. The designed composition has been experimentally validated, and then optimized, using design of experiment methods. About a two-fold increase in the total mAb expression has been observed using this strategy. Our approach can be used in similar bioprocess optimization problems, to suggest new ways of increasing production in different cell factories.

Medienart:

E-Artikel

Erscheinungsjahr:

2020

Erschienen:

2020

Enthalten in:

Zur Gesamtaufnahme - volume:43

Enthalten in:

Bioprocess and biosystems engineering - 43(2020), 8 vom: 24. Aug., Seite 1381-1389

Sprache:

Englisch

Beteiligte Personen:

Fouladiha, Hamideh [VerfasserIn]
Marashi, Sayed-Amir [VerfasserIn]
Torkashvand, Fatemeh [VerfasserIn]
Mahboudi, Fereidoun [VerfasserIn]
Lewis, Nathan E [VerfasserIn]
Vaziri, Behrouz [VerfasserIn]

Links:

Volltext

Themen:

Antibodies, Monoclonal
Central composite design
Constrain-based modeling
Culture Media
DoE
Feeding strategies
Journal Article
Metabolic network models
Plackett–Burman
Recombinant Proteins

Anmerkungen:

Date Completed 09.06.2021

Date Revised 09.06.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1007/s00449-020-02332-6

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM307978494